"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_pegasus import PegasusTokenizer
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			None


SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__:Optional[int]									=			"""▁"""

SCREAMING_SNAKE_CASE__:Optional[Any]									=			{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Tuple									=			{
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

SCREAMING_SNAKE_CASE__:int									=			{
    """google/pegasus-xsum""": 512,
}






class  snake_case__      (       __lowerCAmelCase       ):
 _snake_case       :					Dict							  =						VOCAB_FILES_NAMES
 _snake_case       :					int							  =						PRETRAINED_VOCAB_FILES_MAP
 _snake_case       :					List[str]							  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case       :					Dict							  =						PegasusTokenizer
 _snake_case       :					Any							  =						['''input_ids''', '''attention_mask''']


 def __init__( self    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase="<pad>"    ,		lowerCamelCase="</s>"    ,		lowerCamelCase="<unk>"    ,		lowerCamelCase="<mask_2>"    ,		lowerCamelCase="<mask_1>"    ,		lowerCamelCase=None    ,		lowerCamelCase=103    ,		**lowerCamelCase    ,		):
        __a       =    offset

        if additional_special_tokens is not None:
               if not isinstance(_lowerCamelCase    ,		_lowerCamelCase						):
                      raise TypeError(
                          F"additional_special_tokens should be of type {type(_lowerCamelCase						)}, but is"
                          F" {type(_lowerCamelCase						)}"						)

               __a       =    (
                   ([mask_token_sent] + additional_special_tokens)
                   if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                   else additional_special_tokens
               )
               # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
               additional_special_tokens_extended += [
                   F"<unk_{i}>" for i in range(len(_lowerCamelCase						)    ,		self.offset - 1						)
               ]

               if len(set(_lowerCamelCase						)						) != len(_lowerCamelCase						):
                      raise ValueError(
                          "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                          F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."						)
               __a       =    additional_special_tokens_extended
        else:
               __a       =    [mask_token_sent] if mask_token_sent is not None else []
               additional_special_tokens += [F"<unk_{i}>" for i in range(2    ,		self.offset						)]

        super().__init__(
            _lowerCamelCase    ,		tokenizer_file=_lowerCamelCase    ,		pad_token=_lowerCamelCase    ,		eos_token=_lowerCamelCase    ,		unk_token=_lowerCamelCase    ,		mask_token=_lowerCamelCase    ,		mask_token_sent=_lowerCamelCase    ,		offset=_lowerCamelCase    ,		additional_special_tokens=_lowerCamelCase    ,		**_lowerCamelCase    ,		)
        __a       =    vocab_file
        __a       =    False if not self.vocab_file else True


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    set(self.all_special_ids						)  # call it once instead of inside list comp
        all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special

        if all_special_ids != set(range(len(self.additional_special_tokens						) + 3						)						):
               raise ValueError(
                   "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                   F" {len(self.additional_special_tokens						)} additional_special_tokens, but got {all_special_ids}"						)

        return [1 if x in all_special_ids else 0 for x in seq]


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = False						):
        if already_has_special_tokens:
               return self._special_token_mask(_lowerCamelCase						)
        elif token_ids_a is None:
               return self._special_token_mask(_lowerCamelCase						) + [1]
        else:
               return self._special_token_mask(token_ids_a + token_ids_a						) + [1]


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=None						):
        if token_ids_a is None:
               return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None						):
        if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."						)

        if not os.path.isdir(_lowerCamelCase						):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
               return
        __a       =    os.path.join(
            _lowerCamelCase    ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

        if os.path.abspath(self.vocab_file						) != os.path.abspath(_lowerCamelCase						):
               copyfile(self.vocab_file    ,		_lowerCamelCase						)

        return (out_vocab_file,)

"""simple docstring"""


import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       **__lowerCAmelCase							)				->   List[str]:




						'''simple docstring'''



						lowerCamelCase__   =AutoConfig.from_pretrained(__lowerCAmelCase  ,       **__lowerCAmelCase							)
						lowerCamelCase__   =AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase							)
						model.save_pretrained(__lowerCAmelCase							)
						AutoTokenizer.from_pretrained(__lowerCAmelCase							).save_pretrained(__lowerCAmelCase							)
						return model


if __name__ == "__main__":
	fire.Fire(save_randomly_initialized_version)


